Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

Abstract

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying existing research and by identifying gaps and opportunities for further research. We have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but still seems to be far from its peak.

Tacit knowledge elicitation process for industry 4.0

“…Concepts formation Concept formation is the construction of the ontology (the data graph schema) in a bottom-up approach that extracts knowledge instances from input textual data (ontology learning) and uses other knowledge resources coming from the CMs [45]. The operational KG is then created by ingesting data to the fused ontologies [46][47][48]. The interplay between CMs and the raw KGs leads to concepts formation by merging the knowledge representation for CMs and the knowledge representation for KGs (the ontologies).…”

Semantic Data Mining in Ubiquitous Sensing: A Survey

“…A comprehensive review on usage knowledge graphs in Industry 4.0 was presented in [ 136 ]. In [ 124 , 125 ], the authors present an approach for knowledge fusion in manufacturing operations with the use of knowledge graph embedding methods.…”

Tacit Knowledge Elicitation Process for Industry 4.0

“…Concept formation is the construction of the ontology (the data graph schema) in a bottom-up approach that extracts knowledge instances from input textual data (ontology learning) and uses other knowledge resources coming from the CMs [28]. The whole KG is then created by ingesting data to the fused ontologies [29][30][31]. KGs are more abstract than CMs and allow unlimited connection of concepts in a flexible dynamic data structure.…”